Cost Aggregation and Depth Prediction
After the construction of the cost volume, multi-scale 3D CNN is used to aggregate different levels of spatial context information through the hourglass-shape encoding and decoding network. It has been shown this kind of cost aggregation module helps to regularize noises in ambiguous regions caused by occlusions, textureless surfaces, and to improve final prediction quality [1]}, [2]}, [3]}. Finally, we regress the depth value at each level \(l\) :
\(&\frac{1}{d_{pred, l}} = \frac{1}{d_{min, l}} + (\frac{1}{d_{min, l}}-\frac{1}{d_{max, l}})\frac{k_l}{D_l-1} \\&k_l = \sum ^{D_l-1}_{j=0} \sigma (p_j) \times (v_{l}\times j)\) 
